Cheetah Optimization for Optimal Sizing and Placement of Distributed Generation and Capacitors

Renewable energy sources and energy efficiency has emerged as the backbone of sustainable energy solutions in response to the rising tide of concern over energy security and climate change. Efforts to decrease power losses are part of this. This paper investigates the application of the Cheetah optimizer, a modern metaheuristic optimization algorithm, for the optimal sizing and placement of decentralized generation units such as shunt capacitors in radial distribution networks. The primary objectives are to minimize power losses and enhance voltage profiles under varying load conditions. Unlike conventional methods, Cheetah Optimizer employs three innovative strategies with different randomization techniques to achieve a superior balance between exploration and exploitation. The algorithm's performance is validated on the IEEE 33-bus and IEEE 69-bus distribution test systems. This investigation primarily adds value by applying the methodology to the Adrar electricity distribution network in southern Algeria, demonstrating the effectiveness of these methodologies in real-world systems. Comparative analysis demonstrates that the proposed approach achieves significant improvements in loss reduction and voltage stability compared to state-of-the-art algorithms. Key findings indicate that the Cheetah optimizer delivers robust performance across different network scenarios, with a reduction in power losses by up to 37.34 % and 66.11 % on test systems and enhanced voltage stability by increasing the minimum voltage to permissible levels. Sensitivity analysis further confirms the algorithm's reliability under varying parameter settings. These results underscore the potential of the proposed approach for real-world implementation in optimizing distribution networks.